Content kit · all formats

Every format. Brand-locked.

Twitter / X posts, newsletter templates, infographic cards, PDF report covers — all built on the GTMhub design system. Plus a multi-channel posting cadence so the content actually ships every week.

01 · Twitter / X

Short-form, signal-rich.

Four templates. Hook-first, no threads padded with fluff. Each one earns its 280 characters. Mix across the week.

GTMhub
@gtmhub_ai · 2h
Unpopular truth about "AI for sales": Most of the lift comes from the data layer, not the model. GPT-5 generating first-line personalization on a stale account list is just faster waste. Build the signal layer first. Models second.
↻ 42♥ 218↗ 89K
GTMhub
@gtmhub_ai · 6h
A Series B fintech we worked with: · Reply rate: 0.7% → 2.4% · Meetings: 47 → 98 / mo · Cost / meeting: $420 → $160 Most of the lift was the signal layer, not the AI copy. 90-day case study → gtmhub.ai/work
↻ 128♥ 412↗ 140K
GTMhub
@gtmhub_ai · 1d
4 layers of an AI-native GTM stack: 01 · Signal 02 · Agents 03 · Orchestration 04 · Observability You build bottom-up. Always. Skip a layer and you're debugging in a fog. Full breakdown → gtmhub.ai/labs
↻ 78♥ 304↗ 102K
GTMhub
@gtmhub_ai · 2d
Two questions to ask every "AI for sales" vendor: 1. Where does the data come from? 2. What happens to your output when reputation slips? If they can't answer #2, they don't run in production. They demo.
↻ 64♥ 287↗ 76K
02 · Newsletter

One framework. Every Tuesday.

A weekly newsletter — single piece of long-form, two pull-outs, one tool of the week, one CTA. Built for inbox readability, not engagement bait.

▾ Inbox · email previewopened · 12s ago
GTMhub Tue · May 12
▾ Issue 24 · GTMhub Labs

The signal layer is the actual moat.

Why most "AI for sales" tools fail the moment they hit a stale CRM — and the boring infrastructure that beats the model.

▾ This week's piece

Most operators we meet have the same broken assumption: that the model is the bottleneck. It almost never is. The data your model can read is the bottleneck. And that's a signal-layer problem, not an LLM problem.

We break down four signals you can build today using only your existing CRM + warehouse, no new vendors.

Read on the Labs →

▾ Field note

A client added "funding round in last 90 days" as a single signal. Reply rate on triggered accounts jumped from 1.1% to 3.8% — in two weeks. The agent didn't change.

▾ Tool of the week

Clearbit Reveal → de-anonymize web visitors against your CRM, write firmographics back to Snowflake. We use it on every engagement.

Need a diagnostic Sprint?
$7.5K · 2 weeks · 90-day roadmap.
Talk to us →
▾ structure
  • /Issue number + date · top-line, mono
  • /Title + dek · one piece, one frame
  • /This week's piece · 2-paragraph hook + link
  • /Field note · 1 anonymized client win
  • /Tool of the week · 1 tool we actually use
  • /CTA card · ink block, signal CTA
▾ tone rules
  • /Plain English · no jargon
  • /One frame · don't pile
  • /One link · "Read on the Labs"
  • /Real numbers · real anonymity
03 · Infographic cards

Single-stat. Single-frame.

Four card formats for sharing data, frameworks, and quick insights — sized to LinkedIn, X, Instagram. Each one tells one thing well.

GTMhub.ai
▾ stat callout
Inbox placement at 89%, on average.
89%
Inbox placement
40+
Providers tested
GTMhub.ai
▾ framework
4 layers of an AI-native GTM stack.
01 Signal · 02 Agents · 03 Orchestration · 04 Observability
GTMhub.ai
▾ bar chart
Reply rate by signal type (fintech outbound · 90d)
No signal
Web visit
Hiring
Tech add
Funding
▾ source: 12 client engagements
GTMhub.ai
▾ pull quote
"The data was in Snowflake. We just didn't have the operating layer to turn it into a sequence."
▾ Head of GTM · Series B Fintech
04 · PDF report covers

Long-form gates. Properly designed.

Three cover templates for lead-magnet reports, state-of-X studies, and quarterly reviews. Print-ready, brand-locked. Full inside-page layout in the live report template.

GTMhub
Report · v1 · May 2026

The state of B2B AI outbound, 2026.

62 pages · 14 clients · 240K sends analyzed
GTMhub
Framework · v2

The AI-native GTM stack: a working architecture.

28 pages · framework · open
▾ Free · ungated
GTMhub
Q-Report · Q3

Pipeline benchmarks for B2B SaaS, fintech, and logistics.

48 pages · benchmarks · gated
▾ Gated · work email required
05 · Posting cadence

5 channels. 1 weekly rhythm.

A repeatable weekly schedule across LinkedIn, X, newsletter, blog/Labs, and YouTube. Less is more — cadence beats volume.

MON
LinkedIn · Labs
Carousel · framework
4–8 slide deep dive. End with the Sprint CTA. The post that builds trust.
→ open carousel template
TUE
Newsletter · email
Weekly issue
One piece + field note + tool of the week. Send 10am their time. Single CTA.
→ open newsletter template
WED
LinkedIn · X · IG
Single-image · proof
Anonymized client stat. Links to case study. Image is the message.
→ open infographic card
THU
X · LinkedIn
Short post · unpopular take
280 chars. Counter-intuitive frame. Don't pile. One link if any.
→ open X template
FRI
Labs · blog post
Long-form publish
Long-form goes live on Labs Friday morning. Newsletter teases it Tuesday.
→ open Labs article template
MON · MONTHLY
PDF · ungated
Framework or benchmark report
One long-form report per month. Either ungated frameworks or gated benchmarks.
→ open report template
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All of this, in flight.

Content & Demand engagements include the full multi-channel pipeline — research, writing, design, posting, response, attribution. Templates only get you started. We run the engine.

See Content & Demand